Density Adaptive Attention-based Speech Network: Enhancing Feature Understanding for Mental Health Disorders
Georgios Ioannides, Adrian Kieback, Aman Chadha, Aaron Elkins
2024-09-04
Summary
This paper talks about a new approach called Density Adaptive Attention-based Speech Network, which helps detect depression by analyzing speech patterns more effectively.
What's the problem?
Detecting depression through speech is challenging because it varies greatly from person to person, and there isn't enough data available to train models effectively. Existing methods often struggle to accurately identify depression due to these complexities.
What's the solution?
The researchers developed two models, DAAMAudioCNNLSTM and DAAMAudioTransformer, that use advanced techniques to focus on important parts of speech. The DAAM (Density Adaptive Attention Mechanism) helps the models pay attention to the most informative segments of speech, improving their ability to detect signs of depression. These models were tested on a specific dataset and showed better performance than previous methods without needing extra information like speaker details.
Why it matters?
This research is important because it provides a more reliable way to detect mental health issues through speech analysis. By improving the accuracy and efficiency of depression detection, these models can lead to better support and interventions for individuals struggling with mental health disorders.
Abstract
Speech-based depression detection poses significant challenges for automated detection due to its unique manifestation across individuals and data scarcity. Addressing these challenges, we introduce DAAMAudioCNNLSTM and DAAMAudioTransformer, two parameter efficient and explainable models for audio feature extraction and depression detection. DAAMAudioCNNLSTM features a novel CNN-LSTM framework with multi-head Density Adaptive Attention Mechanism (DAAM), focusing dynamically on informative speech segments. DAAMAudioTransformer, leveraging a transformer encoder in place of the CNN-LSTM architecture, incorporates the same DAAM module for enhanced attention and interpretability. These approaches not only enhance detection robustness and interpretability but also achieve state-of-the-art performance: DAAMAudioCNNLSTM with an F1 macro score of 0.702 and DAAMAudioTransformer with an F1 macro score of 0.72 on the DAIC-WOZ dataset, without reliance on supplementary information such as vowel positions and speaker information during training/validation as in previous approaches. Both models' significant explainability and efficiency in leveraging speech signals for depression detection represent a leap towards more reliable, clinically useful diagnostic tools, promising advancements in speech and mental health care. To foster further research in this domain, we make our code publicly available.